{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 导入工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import Imputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpath = './data/'\n",
    "dname = 'diabetes.csv'\n",
    "data = pd.read_csv(dpath + dname)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 数据基本信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数据的统计信息\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看信息大致形式\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive sample count: 268\n",
      "Negative sample count: 500\n"
     ]
    }
   ],
   "source": [
    "# 查看正样本和负样本的比例\n",
    "print(\"Positive sample count:\",sum(data['Outcome'] == 1))\n",
    "print(\"Negative sample count:\",sum(data['Outcome'] == 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "样本有些不均"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "# 查看各列空值数和类型\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大部分数据为int格式，没有object，比较好处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Pregnancies')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x903ae170f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.countplot(data.Pregnancies.values)\n",
    "plt.xlabel(\"Pregnancies\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Pregnancies')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x903cea8a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]),data.Pregnancies.values, color = \"purple\")\n",
    "plt.title(\"Pregnancies\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(data['Pregnancies'] > 10)\n",
    "#data.loc[data['Pregnancies'] > 10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "感觉怀孕记录比较奇怪，四分之三分位数是6，但是最大值是17，超过10的有34条记录。。可能有些老外特别能生。。？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Glucose')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x903cf8a860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.distplot(data.Glucose.values, bins = 30, kde = True)\n",
    "plt.xlabel(\"Glucose\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Glucose')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x903d02dcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]),data[\"Glucose\"].values, color = \"purple\")\n",
    "plt.title(\"Glucose\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(data[\"Glucose\"] == 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Glucose 数据比较符合正态分布，有部分0值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x903d087c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.distplot(data.BloodPressure.values, bins = 30, kde = True)\n",
    "plt.xlabel(\"Blood Pressure\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Blood Pressure')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x903ceb3128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]),data[\"BloodPressure\"].values, color = \"purple\")\n",
    "plt.title(\"Blood Pressure\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(data[\"BloodPressure\"] == 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "血压数据比较符合正态分布，大部分数据集中在均值附近，但是0值也比较多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x903d19e6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.distplot(data.SkinThickness.values, bins = 30, kde = True)\n",
    "plt.xlabel(\"Skin Thickness\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Skin Thickness')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x903d2444a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]),data[\"SkinThickness\"].values, color = \"purple\")\n",
    "plt.title(\"Skin Thickness\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "227"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(data[\"SkinThickness\"] == 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0值较多，还有一个值很大的离群点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x903d2a0d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.distplot(data.Insulin.values, bins = 30, kde = True)\n",
    "plt.xlabel(\"Insulin\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x903d3984e0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x903d35a550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]),data[\"Insulin\"].values, color = \"purple\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "374"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(data[\"Insulin\"] == 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0值很多，方差也比较大，离群点不好判断。数据中位数为30，四分之三分位数为127，而最大值为846，差异较大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x903d37f4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.distplot(data[\"BMI\"].values, bins = 30, kde = True)\n",
    "plt.xlabel(\"BMI\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'BMI')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x903d45a9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]),data.BMI.values, color = \"purple\")\n",
    "plt.title(\"BMI\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(data[\"BMI\"] == 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "体重指数分布比较集中，只有少数0值点和一个值很高的离群点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x903d4bddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.distplot(data.DiabetesPedigreeFunction.values, bins = 30, kde = True)\n",
    "plt.xlabel(\"Diabetes Pedigree Function\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Diabetes Pedigree Function')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x903d565eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]), data[\"DiabetesPedigreeFunction\"].values, color = \"purple\")\n",
    "plt.title(\"Diabetes Pedigree Function\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大部分数据集中在0到1之间，有一个值很大的离群点，柱图右偏"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x903d5c8a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "#sns.countplot(data[\"Age\"].values)\n",
    "sns.distplot(data[\"Age\"].values, bins = 30, kde = True)\n",
    "plt.xlabel(\"Age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Age')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x903d66ab00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]), data[\"Age\"].values, color = \"purple\")\n",
    "plt.title(\"Age\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据比较分散，柱图右偏，没有缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cols = data.columns.drop(\"Outcome\")\n",
    "cols = data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_corr = data.drop(\"Outcome\",axis = 1).corr().abs()\n",
    "data_corr = data.corr().abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pregnancies</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129459</td>\n",
       "      <td>0.141282</td>\n",
       "      <td>0.081672</td>\n",
       "      <td>0.073535</td>\n",
       "      <td>0.017683</td>\n",
       "      <td>0.033523</td>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.221898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</th>\n",
       "      <td>0.129459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.466581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BloodPressure</th>\n",
       "      <td>0.141282</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>0.065068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinThickness</th>\n",
       "      <td>0.081672</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>0.113970</td>\n",
       "      <td>0.074752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insulin</th>\n",
       "      <td>0.073535</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>0.042163</td>\n",
       "      <td>0.130548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>0.017683</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.292695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <td>0.033523</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>0.173844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>0.113970</td>\n",
       "      <td>0.042163</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.238356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <td>0.221898</td>\n",
       "      <td>0.466581</td>\n",
       "      <td>0.065068</td>\n",
       "      <td>0.074752</td>\n",
       "      <td>0.130548</td>\n",
       "      <td>0.292695</td>\n",
       "      <td>0.173844</td>\n",
       "      <td>0.238356</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Pregnancies   Glucose  BloodPressure  SkinThickness  \\\n",
       "Pregnancies                  1.000000  0.129459       0.141282       0.081672   \n",
       "Glucose                      0.129459  1.000000       0.152590       0.057328   \n",
       "BloodPressure                0.141282  0.152590       1.000000       0.207371   \n",
       "SkinThickness                0.081672  0.057328       0.207371       1.000000   \n",
       "Insulin                      0.073535  0.331357       0.088933       0.436783   \n",
       "BMI                          0.017683  0.221071       0.281805       0.392573   \n",
       "DiabetesPedigreeFunction     0.033523  0.137337       0.041265       0.183928   \n",
       "Age                          0.544341  0.263514       0.239528       0.113970   \n",
       "Outcome                      0.221898  0.466581       0.065068       0.074752   \n",
       "\n",
       "                           Insulin       BMI  DiabetesPedigreeFunction  \\\n",
       "Pregnancies               0.073535  0.017683                  0.033523   \n",
       "Glucose                   0.331357  0.221071                  0.137337   \n",
       "BloodPressure             0.088933  0.281805                  0.041265   \n",
       "SkinThickness             0.436783  0.392573                  0.183928   \n",
       "Insulin                   1.000000  0.197859                  0.185071   \n",
       "BMI                       0.197859  1.000000                  0.140647   \n",
       "DiabetesPedigreeFunction  0.185071  0.140647                  1.000000   \n",
       "Age                       0.042163  0.036242                  0.033561   \n",
       "Outcome                   0.130548  0.292695                  0.173844   \n",
       "\n",
       "                               Age   Outcome  \n",
       "Pregnancies               0.544341  0.221898  \n",
       "Glucose                   0.263514  0.466581  \n",
       "BloodPressure             0.239528  0.065068  \n",
       "SkinThickness             0.113970  0.074752  \n",
       "Insulin                   0.042163  0.130548  \n",
       "BMI                       0.036242  0.292695  \n",
       "DiabetesPedigreeFunction  0.033561  0.173844  \n",
       "Age                       1.000000  0.238356  \n",
       "Outcome                   0.238356  1.000000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x903d6417b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots(figsize=(10,9))\n",
    "sns.heatmap(data_corr, annot = True)\n",
    "sns.heatmap(data_corr, mask = data_corr < 1, cbar = False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies and Age = 0.544\n"
     ]
    }
   ],
   "source": [
    "threashold = 0.5\n",
    "\n",
    "corr_list = []\n",
    "size = cols.shape[0]\n",
    "\n",
    "for i in range(0, size):\n",
    "    for j in range(i+1, size):\n",
    "        if data_corr.iloc[i,j] > threashold:\n",
    "            corr_list.append([data_corr.iloc[i,j],i,j])\n",
    "            \n",
    "s_corr_list = sorted(corr_list, key = lambda x : -abs(x[0]))\n",
    "\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.3f\" % (cols[i],cols[j],v));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x903d654550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for v,i,j in s_corr_list:\n",
    "    sns.pairplot(data_corr, size = 6, x_vars = cols[i], y_vars = cols[j])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "相关性较强的只有年龄和怀孕次数，所有特征和输出的相关性都不强"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>116</td>\n",
       "      <td>74</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0.201</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>88</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.248</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>115</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.134</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>197</td>\n",
       "      <td>70</td>\n",
       "      <td>45</td>\n",
       "      <td>543</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.158</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>125</td>\n",
       "      <td>96</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.232</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4</td>\n",
       "      <td>110</td>\n",
       "      <td>92</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>37.6</td>\n",
       "      <td>0.191</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10</td>\n",
       "      <td>168</td>\n",
       "      <td>74</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.537</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10</td>\n",
       "      <td>139</td>\n",
       "      <td>80</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>27.1</td>\n",
       "      <td>1.441</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>189</td>\n",
       "      <td>60</td>\n",
       "      <td>23</td>\n",
       "      <td>846</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.398</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5</td>\n",
       "      <td>166</td>\n",
       "      <td>72</td>\n",
       "      <td>19</td>\n",
       "      <td>175</td>\n",
       "      <td>25.8</td>\n",
       "      <td>0.587</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7</td>\n",
       "      <td>100</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.484</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>84</td>\n",
       "      <td>47</td>\n",
       "      <td>230</td>\n",
       "      <td>45.8</td>\n",
       "      <td>0.551</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7</td>\n",
       "      <td>107</td>\n",
       "      <td>74</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>29.6</td>\n",
       "      <td>0.254</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>103</td>\n",
       "      <td>30</td>\n",
       "      <td>38</td>\n",
       "      <td>83</td>\n",
       "      <td>43.3</td>\n",
       "      <td>0.183</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>70</td>\n",
       "      <td>30</td>\n",
       "      <td>96</td>\n",
       "      <td>34.6</td>\n",
       "      <td>0.529</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3</td>\n",
       "      <td>126</td>\n",
       "      <td>88</td>\n",
       "      <td>41</td>\n",
       "      <td>235</td>\n",
       "      <td>39.3</td>\n",
       "      <td>0.704</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>8</td>\n",
       "      <td>99</td>\n",
       "      <td>84</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>35.4</td>\n",
       "      <td>0.388</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7</td>\n",
       "      <td>196</td>\n",
       "      <td>90</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>39.8</td>\n",
       "      <td>0.451</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>9</td>\n",
       "      <td>119</td>\n",
       "      <td>80</td>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.263</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>11</td>\n",
       "      <td>143</td>\n",
       "      <td>94</td>\n",
       "      <td>33</td>\n",
       "      <td>146</td>\n",
       "      <td>36.6</td>\n",
       "      <td>0.254</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>10</td>\n",
       "      <td>125</td>\n",
       "      <td>70</td>\n",
       "      <td>26</td>\n",
       "      <td>115</td>\n",
       "      <td>31.1</td>\n",
       "      <td>0.205</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>7</td>\n",
       "      <td>147</td>\n",
       "      <td>76</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>39.4</td>\n",
       "      <td>0.257</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>66</td>\n",
       "      <td>15</td>\n",
       "      <td>140</td>\n",
       "      <td>23.2</td>\n",
       "      <td>0.487</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>13</td>\n",
       "      <td>145</td>\n",
       "      <td>82</td>\n",
       "      <td>19</td>\n",
       "      <td>110</td>\n",
       "      <td>22.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5</td>\n",
       "      <td>117</td>\n",
       "      <td>92</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>34.1</td>\n",
       "      <td>0.337</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>2</td>\n",
       "      <td>99</td>\n",
       "      <td>60</td>\n",
       "      <td>17</td>\n",
       "      <td>160</td>\n",
       "      <td>36.6</td>\n",
       "      <td>0.453</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>1</td>\n",
       "      <td>102</td>\n",
       "      <td>74</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0.293</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>11</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>37</td>\n",
       "      <td>150</td>\n",
       "      <td>42.3</td>\n",
       "      <td>0.785</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>3</td>\n",
       "      <td>102</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>94</td>\n",
       "      <td>30.8</td>\n",
       "      <td>0.400</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>58</td>\n",
       "      <td>18</td>\n",
       "      <td>116</td>\n",
       "      <td>28.5</td>\n",
       "      <td>0.219</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>9</td>\n",
       "      <td>140</td>\n",
       "      <td>94</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>32.7</td>\n",
       "      <td>0.734</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>13</td>\n",
       "      <td>153</td>\n",
       "      <td>88</td>\n",
       "      <td>37</td>\n",
       "      <td>140</td>\n",
       "      <td>40.6</td>\n",
       "      <td>1.174</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>12</td>\n",
       "      <td>100</td>\n",
       "      <td>84</td>\n",
       "      <td>33</td>\n",
       "      <td>105</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.488</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>1</td>\n",
       "      <td>147</td>\n",
       "      <td>94</td>\n",
       "      <td>41</td>\n",
       "      <td>30</td>\n",
       "      <td>49.3</td>\n",
       "      <td>0.358</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>74</td>\n",
       "      <td>41</td>\n",
       "      <td>57</td>\n",
       "      <td>46.3</td>\n",
       "      <td>1.096</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>3</td>\n",
       "      <td>187</td>\n",
       "      <td>70</td>\n",
       "      <td>22</td>\n",
       "      <td>200</td>\n",
       "      <td>36.4</td>\n",
       "      <td>0.408</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>6</td>\n",
       "      <td>162</td>\n",
       "      <td>62</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>24.3</td>\n",
       "      <td>0.178</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>4</td>\n",
       "      <td>136</td>\n",
       "      <td>70</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>31.2</td>\n",
       "      <td>1.182</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>1</td>\n",
       "      <td>121</td>\n",
       "      <td>78</td>\n",
       "      <td>39</td>\n",
       "      <td>74</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.261</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>3</td>\n",
       "      <td>108</td>\n",
       "      <td>62</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.223</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>0</td>\n",
       "      <td>181</td>\n",
       "      <td>88</td>\n",
       "      <td>44</td>\n",
       "      <td>510</td>\n",
       "      <td>43.3</td>\n",
       "      <td>0.222</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>8</td>\n",
       "      <td>154</td>\n",
       "      <td>78</td>\n",
       "      <td>32</td>\n",
       "      <td>30</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.443</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>88</td>\n",
       "      <td>39</td>\n",
       "      <td>110</td>\n",
       "      <td>36.5</td>\n",
       "      <td>1.057</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>7</td>\n",
       "      <td>137</td>\n",
       "      <td>90</td>\n",
       "      <td>41</td>\n",
       "      <td>30</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.391</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>36.3</td>\n",
       "      <td>0.258</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>76</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>37.5</td>\n",
       "      <td>0.197</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>6</td>\n",
       "      <td>190</td>\n",
       "      <td>92</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>35.5</td>\n",
       "      <td>0.278</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>2</td>\n",
       "      <td>88</td>\n",
       "      <td>58</td>\n",
       "      <td>26</td>\n",
       "      <td>16</td>\n",
       "      <td>28.4</td>\n",
       "      <td>0.766</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>9</td>\n",
       "      <td>170</td>\n",
       "      <td>74</td>\n",
       "      <td>31</td>\n",
       "      <td>30</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.403</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>9</td>\n",
       "      <td>89</td>\n",
       "      <td>62</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0.142</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>30</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>30</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35       30  33.6   \n",
       "1              1       85             66             29       30  26.6   \n",
       "2              8      183             64             23       30  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "5              5      116             74             23       30  25.6   \n",
       "6              3       78             50             32       88  31.0   \n",
       "7             10      115             72             23       30  35.3   \n",
       "8              2      197             70             45      543  30.5   \n",
       "9              8      125             96             23       30  32.0   \n",
       "10             4      110             92             23       30  37.6   \n",
       "11            10      168             74             23       30  38.0   \n",
       "12            10      139             80             23       30  27.1   \n",
       "13             1      189             60             23      846  30.1   \n",
       "14             5      166             72             19      175  25.8   \n",
       "15             7      100             72             23       30  30.0   \n",
       "16             0      118             84             47      230  45.8   \n",
       "17             7      107             74             23       30  29.6   \n",
       "18             1      103             30             38       83  43.3   \n",
       "19             1      115             70             30       96  34.6   \n",
       "20             3      126             88             41      235  39.3   \n",
       "21             8       99             84             23       30  35.4   \n",
       "22             7      196             90             23       30  39.8   \n",
       "23             9      119             80             35       30  29.0   \n",
       "24            11      143             94             33      146  36.6   \n",
       "25            10      125             70             26      115  31.1   \n",
       "26             7      147             76             23       30  39.4   \n",
       "27             1       97             66             15      140  23.2   \n",
       "28            13      145             82             19      110  22.2   \n",
       "29             5      117             92             23       30  34.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "738            2       99             60             17      160  36.6   \n",
       "739            1      102             74             23       30  39.5   \n",
       "740           11      120             80             37      150  42.3   \n",
       "741            3      102             44             20       94  30.8   \n",
       "742            1      109             58             18      116  28.5   \n",
       "743            9      140             94             23       30  32.7   \n",
       "744           13      153             88             37      140  40.6   \n",
       "745           12      100             84             33      105  30.0   \n",
       "746            1      147             94             41       30  49.3   \n",
       "747            1       81             74             41       57  46.3   \n",
       "748            3      187             70             22      200  36.4   \n",
       "749            6      162             62             23       30  24.3   \n",
       "750            4      136             70             23       30  31.2   \n",
       "751            1      121             78             39       74  39.0   \n",
       "752            3      108             62             24       30  26.0   \n",
       "753            0      181             88             44      510  43.3   \n",
       "754            8      154             78             32       30  32.4   \n",
       "755            1      128             88             39      110  36.5   \n",
       "756            7      137             90             41       30  32.0   \n",
       "757            0      123             72             23       30  36.3   \n",
       "758            1      106             76             23       30  37.5   \n",
       "759            6      190             92             23       30  35.5   \n",
       "760            2       88             58             26       16  28.4   \n",
       "761            9      170             74             31       30  44.0   \n",
       "762            9       89             62             23       30  22.5   \n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27       30  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60             23       30  30.1   \n",
       "767            1       93             70             31       30  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "2                       0.672   32        1  \n",
       "3                       0.167   21        0  \n",
       "4                       2.288   33        1  \n",
       "5                       0.201   30        0  \n",
       "6                       0.248   26        1  \n",
       "7                       0.134   29        0  \n",
       "8                       0.158   53        1  \n",
       "9                       0.232   54        1  \n",
       "10                      0.191   30        0  \n",
       "11                      0.537   34        1  \n",
       "12                      1.441   57        0  \n",
       "13                      0.398   59        1  \n",
       "14                      0.587   51        1  \n",
       "15                      0.484   32        1  \n",
       "16                      0.551   31        1  \n",
       "17                      0.254   31        1  \n",
       "18                      0.183   33        0  \n",
       "19                      0.529   32        1  \n",
       "20                      0.704   27        0  \n",
       "21                      0.388   50        0  \n",
       "22                      0.451   41        1  \n",
       "23                      0.263   29        1  \n",
       "24                      0.254   51        1  \n",
       "25                      0.205   41        1  \n",
       "26                      0.257   43        1  \n",
       "27                      0.487   22        0  \n",
       "28                      0.245   57        0  \n",
       "29                      0.337   38        0  \n",
       "..                        ...  ...      ...  \n",
       "738                     0.453   21        0  \n",
       "739                     0.293   42        1  \n",
       "740                     0.785   48        1  \n",
       "741                     0.400   26        0  \n",
       "742                     0.219   22        0  \n",
       "743                     0.734   45        1  \n",
       "744                     1.174   39        0  \n",
       "745                     0.488   46        0  \n",
       "746                     0.358   27        1  \n",
       "747                     1.096   32        0  \n",
       "748                     0.408   36        1  \n",
       "749                     0.178   50        1  \n",
       "750                     1.182   22        1  \n",
       "751                     0.261   28        0  \n",
       "752                     0.223   25        0  \n",
       "753                     0.222   26        1  \n",
       "754                     0.443   45        1  \n",
       "755                     1.057   37        1  \n",
       "756                     0.391   39        0  \n",
       "757                     0.258   52        1  \n",
       "758                     0.197   26        0  \n",
       "759                     0.278   66        1  \n",
       "760                     0.766   22        0  \n",
       "761                     0.403   43        1  \n",
       "762                     0.142   33        0  \n",
       "763                     0.171   63        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用中位数代替0值\n",
    "#def fillZerosWithMean(df):\n",
    "def fillZerosWithMedian(df):\n",
    "    mean_Glucose = round(df[\"Glucose\"].median())\n",
    "    mean_BloodPressure = round(df[\"BloodPressure\"].median())\n",
    "    mean_SkinThickness = round(df[\"SkinThickness\"].median())\n",
    "    mean_Insulin = round(df[\"Insulin\"].median())\n",
    "    mean_BMI = df[\"BMI\"].median()\n",
    "    # imputer = Imputer(missing_values=0, strategy = 'median')\n",
    "    # df[target] = imputer.fit_transform(df[target])\n",
    "    \n",
    "    df.replace({\"Glucose\" : {0 : mean_Glucose},\n",
    "                      \"BloodPressure\" : {0 : mean_BloodPressure},\n",
    "                      \"SkinThickness\" : {0 : mean_SkinThickness},\n",
    "                      \"Insulin\" : {0 : mean_Insulin},\n",
    "                      \"BMI\" : {0 : mean_BMI}\n",
    "                     },inplace = True)\n",
    "    return df\n",
    "\n",
    "fillZerosWithMedian(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "因为Glucose，BloodPressure，SkinThickness，Insulin四项为int类型，使用round进行了取整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:194: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>116</td>\n",
       "      <td>74</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0.201</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>50</td>\n",
       "      <td>32.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.248</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>115</td>\n",
       "      <td>72</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.134</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>197</td>\n",
       "      <td>70</td>\n",
       "      <td>45.0</td>\n",
       "      <td>519.9</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.158</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>125</td>\n",
       "      <td>96</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.232</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4</td>\n",
       "      <td>110</td>\n",
       "      <td>92</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>37.6</td>\n",
       "      <td>0.191</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10</td>\n",
       "      <td>168</td>\n",
       "      <td>74</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.537</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10</td>\n",
       "      <td>139</td>\n",
       "      <td>80</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>27.1</td>\n",
       "      <td>1.441</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>189</td>\n",
       "      <td>60</td>\n",
       "      <td>23.0</td>\n",
       "      <td>519.9</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.398</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5</td>\n",
       "      <td>166</td>\n",
       "      <td>72</td>\n",
       "      <td>19.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>25.8</td>\n",
       "      <td>0.587</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7</td>\n",
       "      <td>100</td>\n",
       "      <td>72</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.484</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>84</td>\n",
       "      <td>47.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>45.8</td>\n",
       "      <td>0.551</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7</td>\n",
       "      <td>107</td>\n",
       "      <td>74</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>29.6</td>\n",
       "      <td>0.254</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>103</td>\n",
       "      <td>30</td>\n",
       "      <td>38.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>43.3</td>\n",
       "      <td>0.183</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>70</td>\n",
       "      <td>30.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>34.6</td>\n",
       "      <td>0.529</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3</td>\n",
       "      <td>126</td>\n",
       "      <td>88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>39.3</td>\n",
       "      <td>0.704</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>8</td>\n",
       "      <td>99</td>\n",
       "      <td>84</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>35.4</td>\n",
       "      <td>0.388</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7</td>\n",
       "      <td>196</td>\n",
       "      <td>90</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>39.8</td>\n",
       "      <td>0.451</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>9</td>\n",
       "      <td>119</td>\n",
       "      <td>80</td>\n",
       "      <td>35.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.263</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>11</td>\n",
       "      <td>143</td>\n",
       "      <td>94</td>\n",
       "      <td>33.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>36.6</td>\n",
       "      <td>0.254</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>10</td>\n",
       "      <td>125</td>\n",
       "      <td>70</td>\n",
       "      <td>26.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>31.1</td>\n",
       "      <td>0.205</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>7</td>\n",
       "      <td>147</td>\n",
       "      <td>76</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>39.4</td>\n",
       "      <td>0.257</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>66</td>\n",
       "      <td>15.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>0.487</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>13</td>\n",
       "      <td>145</td>\n",
       "      <td>82</td>\n",
       "      <td>19.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5</td>\n",
       "      <td>117</td>\n",
       "      <td>92</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>34.1</td>\n",
       "      <td>0.337</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>2</td>\n",
       "      <td>99</td>\n",
       "      <td>60</td>\n",
       "      <td>17.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>36.6</td>\n",
       "      <td>0.453</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>1</td>\n",
       "      <td>102</td>\n",
       "      <td>74</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0.293</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>11</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>37.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>42.3</td>\n",
       "      <td>0.785</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>3</td>\n",
       "      <td>102</td>\n",
       "      <td>44</td>\n",
       "      <td>20.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>30.8</td>\n",
       "      <td>0.400</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>58</td>\n",
       "      <td>18.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>28.5</td>\n",
       "      <td>0.219</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>9</td>\n",
       "      <td>140</td>\n",
       "      <td>94</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>32.7</td>\n",
       "      <td>0.734</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>13</td>\n",
       "      <td>153</td>\n",
       "      <td>88</td>\n",
       "      <td>37.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>40.6</td>\n",
       "      <td>1.174</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>12</td>\n",
       "      <td>100</td>\n",
       "      <td>84</td>\n",
       "      <td>33.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.488</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>1</td>\n",
       "      <td>147</td>\n",
       "      <td>94</td>\n",
       "      <td>41.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>49.3</td>\n",
       "      <td>0.358</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>74</td>\n",
       "      <td>41.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>46.3</td>\n",
       "      <td>1.096</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>3</td>\n",
       "      <td>187</td>\n",
       "      <td>70</td>\n",
       "      <td>22.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>36.4</td>\n",
       "      <td>0.408</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>6</td>\n",
       "      <td>162</td>\n",
       "      <td>62</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>24.3</td>\n",
       "      <td>0.178</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>4</td>\n",
       "      <td>136</td>\n",
       "      <td>70</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31.2</td>\n",
       "      <td>1.182</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>1</td>\n",
       "      <td>121</td>\n",
       "      <td>78</td>\n",
       "      <td>39.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.261</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>3</td>\n",
       "      <td>108</td>\n",
       "      <td>62</td>\n",
       "      <td>24.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.223</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>0</td>\n",
       "      <td>181</td>\n",
       "      <td>88</td>\n",
       "      <td>44.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>43.3</td>\n",
       "      <td>0.222</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>8</td>\n",
       "      <td>154</td>\n",
       "      <td>78</td>\n",
       "      <td>32.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.443</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>88</td>\n",
       "      <td>39.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>1.057</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>7</td>\n",
       "      <td>137</td>\n",
       "      <td>90</td>\n",
       "      <td>41.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.391</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>72</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>36.3</td>\n",
       "      <td>0.258</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>76</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>37.5</td>\n",
       "      <td>0.197</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>6</td>\n",
       "      <td>190</td>\n",
       "      <td>92</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>0.278</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>2</td>\n",
       "      <td>88</td>\n",
       "      <td>58</td>\n",
       "      <td>26.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>28.4</td>\n",
       "      <td>0.766</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>9</td>\n",
       "      <td>170</td>\n",
       "      <td>74</td>\n",
       "      <td>31.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.403</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>9</td>\n",
       "      <td>89</td>\n",
       "      <td>62</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0.142</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72           35.0     30.0  33.6   \n",
       "1              1       85             66           29.0     30.0  26.6   \n",
       "2              8      183             64           23.0     30.0  23.3   \n",
       "3              1       89             66           23.0     94.0  28.1   \n",
       "4              0      137             40           35.0    168.0  43.1   \n",
       "5              5      116             74           23.0     30.0  25.6   \n",
       "6              3       78             50           32.0     88.0  31.0   \n",
       "7             10      115             72           23.0     30.0  35.3   \n",
       "8              2      197             70           45.0    519.9  30.5   \n",
       "9              8      125             96           23.0     30.0  32.0   \n",
       "10             4      110             92           23.0     30.0  37.6   \n",
       "11            10      168             74           23.0     30.0  38.0   \n",
       "12            10      139             80           23.0     30.0  27.1   \n",
       "13             1      189             60           23.0    519.9  30.1   \n",
       "14             5      166             72           19.0    175.0  25.8   \n",
       "15             7      100             72           23.0     30.0  30.0   \n",
       "16             0      118             84           47.0    230.0  45.8   \n",
       "17             7      107             74           23.0     30.0  29.6   \n",
       "18             1      103             30           38.0     83.0  43.3   \n",
       "19             1      115             70           30.0     96.0  34.6   \n",
       "20             3      126             88           41.0    235.0  39.3   \n",
       "21             8       99             84           23.0     30.0  35.4   \n",
       "22             7      196             90           23.0     30.0  39.8   \n",
       "23             9      119             80           35.0     30.0  29.0   \n",
       "24            11      143             94           33.0    146.0  36.6   \n",
       "25            10      125             70           26.0    115.0  31.1   \n",
       "26             7      147             76           23.0     30.0  39.4   \n",
       "27             1       97             66           15.0    140.0  23.2   \n",
       "28            13      145             82           19.0    110.0  22.2   \n",
       "29             5      117             92           23.0     30.0  34.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "738            2       99             60           17.0    160.0  36.6   \n",
       "739            1      102             74           23.0     30.0  39.5   \n",
       "740           11      120             80           37.0    150.0  42.3   \n",
       "741            3      102             44           20.0     94.0  30.8   \n",
       "742            1      109             58           18.0    116.0  28.5   \n",
       "743            9      140             94           23.0     30.0  32.7   \n",
       "744           13      153             88           37.0    140.0  40.6   \n",
       "745           12      100             84           33.0    105.0  30.0   \n",
       "746            1      147             94           41.0     30.0  49.3   \n",
       "747            1       81             74           41.0     57.0  46.3   \n",
       "748            3      187             70           22.0    200.0  36.4   \n",
       "749            6      162             62           23.0     30.0  24.3   \n",
       "750            4      136             70           23.0     30.0  31.2   \n",
       "751            1      121             78           39.0     74.0  39.0   \n",
       "752            3      108             62           24.0     30.0  26.0   \n",
       "753            0      181             88           44.0    510.0  43.3   \n",
       "754            8      154             78           32.0     30.0  32.4   \n",
       "755            1      128             88           39.0    110.0  36.5   \n",
       "756            7      137             90           41.0     30.0  32.0   \n",
       "757            0      123             72           23.0     30.0  36.3   \n",
       "758            1      106             76           23.0     30.0  37.5   \n",
       "759            6      190             92           23.0     30.0  35.5   \n",
       "760            2       88             58           26.0     18.0  28.4   \n",
       "761            9      170             74           31.0     30.0  44.0   \n",
       "762            9       89             62           23.0     30.0  22.5   \n",
       "763           10      101             76           48.0    180.0  32.9   \n",
       "764            2      122             70           27.0     30.0  36.8   \n",
       "765            5      121             72           23.0    112.0  26.2   \n",
       "766            1      126             60           23.0     30.0  30.1   \n",
       "767            1       93             70           31.0     30.0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "2                       0.672   32        1  \n",
       "3                       0.167   21        0  \n",
       "4                       2.288   33        1  \n",
       "5                       0.201   30        0  \n",
       "6                       0.248   26        1  \n",
       "7                       0.134   29        0  \n",
       "8                       0.158   53        1  \n",
       "9                       0.232   54        1  \n",
       "10                      0.191   30        0  \n",
       "11                      0.537   34        1  \n",
       "12                      1.441   57        0  \n",
       "13                      0.398   59        1  \n",
       "14                      0.587   51        1  \n",
       "15                      0.484   32        1  \n",
       "16                      0.551   31        1  \n",
       "17                      0.254   31        1  \n",
       "18                      0.183   33        0  \n",
       "19                      0.529   32        1  \n",
       "20                      0.704   27        0  \n",
       "21                      0.388   50        0  \n",
       "22                      0.451   41        1  \n",
       "23                      0.263   29        1  \n",
       "24                      0.254   51        1  \n",
       "25                      0.205   41        1  \n",
       "26                      0.257   43        1  \n",
       "27                      0.487   22        0  \n",
       "28                      0.245   57        0  \n",
       "29                      0.337   38        0  \n",
       "..                        ...  ...      ...  \n",
       "738                     0.453   21        0  \n",
       "739                     0.293   42        1  \n",
       "740                     0.785   48        1  \n",
       "741                     0.400   26        0  \n",
       "742                     0.219   22        0  \n",
       "743                     0.734   45        1  \n",
       "744                     1.174   39        0  \n",
       "745                     0.488   46        0  \n",
       "746                     0.358   27        1  \n",
       "747                     1.096   32        0  \n",
       "748                     0.408   36        1  \n",
       "749                     0.178   50        1  \n",
       "750                     1.182   22        1  \n",
       "751                     0.261   28        0  \n",
       "752                     0.223   25        0  \n",
       "753                     0.222   26        1  \n",
       "754                     0.443   45        1  \n",
       "755                     1.057   37        1  \n",
       "756                     0.391   39        0  \n",
       "757                     0.258   52        1  \n",
       "758                     0.197   26        0  \n",
       "759                     0.278   66        1  \n",
       "760                     0.766   22        0  \n",
       "761                     0.403   43        1  \n",
       "762                     0.142   33        0  \n",
       "763                     0.171   63        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 处理部分离群点，用分位数替代\n",
    "def replaceOutliersWithIQR(df, columns, ceil, floor):\n",
    "    size = len(columns)\n",
    "    for i in range(0,size):\n",
    "        limit_top = np.percentile(df[columns[i]].values,ceil)\n",
    "        limit_bottom = np.percentile(df[columns[i]].values,floor)\n",
    "        df[columns[i]].loc[df[columns[i]]>limit_top] = limit_top\n",
    "        df[columns[i]].loc[df[columns[i]]<limit_bottom] = limit_bottom\n",
    "    return df\n",
    "\n",
    "columns = [\"SkinThickness\", \"Insulin\", \"BMI\"]\n",
    "replaceOutliersWithIQR(data, columns, 99, 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用99%和0.5%分位数替代了离群点的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train shape: (614, 9)\n",
      "Test shape: (154, 9)\n"
     ]
    }
   ],
   "source": [
    "# 分割训练集和测试集\n",
    "data_train, data_test = train_test_split(data,test_size = 0.2, random_state = 36)\n",
    "\n",
    "# 分离x和y\n",
    "y_train = data_train[\"Outcome\"]\n",
    "X_train = data_train.drop(\"Outcome\", axis = 1)\n",
    "\n",
    "y_test = data_test[\"Outcome\"]\n",
    "X_test = data_test.drop(\"Outcome\", axis = 1)\n",
    "print(\"Train shape:\",data_train.shape)\n",
    "print(\"Test shape:\",data_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 初始化特征的标准化器\n",
    "ss_X = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分割了训练集和测试集，以20%的数据作为测试集，并对数据特征进行标准化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Logistic回归"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1 default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of each fold is:  [0.43633584 0.44835583 0.47335425 0.51915928 0.48488565]\n",
      "cv logloss is: 0.472418171281568\n"
     ]
    }
   ],
   "source": [
    "lr= LogisticRegression()\n",
    "loss = cross_val_score(lr, X_train, y_train, cv=5, scoring='neg_log_loss') # 5折交叉验证\n",
    "print ('logloss of each fold is: ',-loss)\n",
    "print ('cv logloss is:', -loss.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
      "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
      "          verbose=0, warm_start=False)\n",
      "Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.77      0.87      0.82       102\n",
      "          1       0.67      0.50      0.57        52\n",
      "\n",
      "avg / total       0.74      0.75      0.74       154\n",
      "\n",
      "Confusion Matrix:\n",
      " [[89 13]\n",
      " [26 26]]\n"
     ]
    }
   ],
   "source": [
    "lr.fit(X_train, y_train)\n",
    "lr_predict = lr.predict(X_test)\n",
    "print(lr)\n",
    "print(\"Classification report:\\n\",classification_report(y_test,lr_predict))\n",
    "print(\"Confusion Matrix:\\n\",confusion_matrix(y_test, lr_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "默认的logistic回归使用了L2正则，C为1，solver使用了liblinear。\n",
    "在测试集上的效果一般，类别为0的数据召回率很高，但是类别为1的数据有一半错分成了0，可能是因为原数据中类别为0的数据占比较多并且没有进行采样处理，如果调整class weight可能效果会好些。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 网格搜索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 网格搜索\n",
    "penaltys = ['l1','l2']\n",
    "penaltys_1 = ['l2']\n",
    "penaltys_2 = ['l1']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "Cs_1 = [0.1, 0.2, 0.3, 0.4 , 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 3, 4, 5]\n",
    "Cs_2 = [0.11, 0.12, 0.13, 0.14 , 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.35, 0.4, 0.45]\n",
    "Cs_3 = [0.2, 0.201, 0.202, 0.203, 0.204, 0.205, 0.206, 0.207, 0.208, 0.209, 0.21, 0.211, 0.212, 0.213, 0.214, 0.215, 0.216, 0.217, 0.218, 0.219]\n",
    "\n",
    "parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_grid = LogisticRegression()\n",
    "grid = GridSearchCV(lr_grid, parameters, cv = 5, scoring ='neg_log_loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV(cv=5, error_score='raise',\n",
      "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
      "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
      "          verbose=0, warm_start=False),\n",
      "       fit_params=None, iid=True, n_jobs=1,\n",
      "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
      "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
      "       scoring='neg_log_loss', verbose=0)\n",
      "Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.77      0.87      0.82       102\n",
      "          1       0.66      0.48      0.56        52\n",
      "\n",
      "avg / total       0.73      0.74      0.73       154\n",
      "\n",
      "Confusion Matrix:\n",
      " [[89 13]\n",
      " [27 25]]\n"
     ]
    }
   ],
   "source": [
    "grid.fit(X_train, y_train)\n",
    "lr_grid_predict = grid.predict(X_test)\n",
    "print(grid)\n",
    "print(\"Classification report:\\n\",classification_report(y_test,lr_grid_predict))\n",
    "print(\"Confusion Matrix:\\n\",confusion_matrix(y_test, lr_grid_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.00120096, 0.00100088, 0.00100069, 0.00240169, 0.00140119,\n",
       "        0.0020021 , 0.00160089, 0.00120115, 0.00140104, 0.00160103,\n",
       "        0.00140166, 0.00140104, 0.00160103, 0.0010006 ]),\n",
       " 'mean_score_time': array([0.00060077, 0.00120134, 0.0008009 , 0.0012013 , 0.00100026,\n",
       "        0.00120091, 0.00120087, 0.00100045, 0.0010006 , 0.0008009 ,\n",
       "        0.00099993, 0.00080028, 0.00080042, 0.00100069]),\n",
       " 'mean_test_score': array([-0.69314718, -0.63757323, -0.66550348, -0.52052159, -0.47354272,\n",
       "        -0.47265803, -0.47194208, -0.47226297, -0.47279709, -0.4728254 ,\n",
       "        -0.47288653, -0.47289283, -0.47290107, -0.47289969]),\n",
       " 'mean_train_score': array([-0.69314718, -0.63627699, -0.66506437, -0.51500755, -0.46566992,\n",
       "        -0.45993463, -0.45570717, -0.45559037, -0.45551443, -0.45551314,\n",
       "        -0.45551231, -0.4555123 , -0.45551229, -0.45551229]),\n",
       " 'param_C': masked_array(data=[0.001, 0.001, 0.01, 0.01, 0.1, 0.1, 1, 1, 10, 10, 100,\n",
       "                    100, 1000, 1000],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_penalty': masked_array(data=['l1', 'l2', 'l1', 'l2', 'l1', 'l2', 'l1', 'l2', 'l1',\n",
       "                    'l2', 'l1', 'l2', 'l1', 'l2'],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'C': 0.001, 'penalty': 'l1'},\n",
       "  {'C': 0.001, 'penalty': 'l2'},\n",
       "  {'C': 0.01, 'penalty': 'l1'},\n",
       "  {'C': 0.01, 'penalty': 'l2'},\n",
       "  {'C': 0.1, 'penalty': 'l1'},\n",
       "  {'C': 0.1, 'penalty': 'l2'},\n",
       "  {'C': 1, 'penalty': 'l1'},\n",
       "  {'C': 1, 'penalty': 'l2'},\n",
       "  {'C': 10, 'penalty': 'l1'},\n",
       "  {'C': 10, 'penalty': 'l2'},\n",
       "  {'C': 100, 'penalty': 'l1'},\n",
       "  {'C': 100, 'penalty': 'l2'},\n",
       "  {'C': 1000, 'penalty': 'l1'},\n",
       "  {'C': 1000, 'penalty': 'l2'}],\n",
       " 'rank_test_score': array([14, 12, 13, 11, 10,  3,  1,  2,  4,  5,  6,  7,  9,  8]),\n",
       " 'split0_test_score': array([-0.69314718, -0.63493687, -0.66514858, -0.50719867, -0.44951247,\n",
       "        -0.4441603 , -0.43682711, -0.43633584, -0.43580652, -0.43571497,\n",
       "        -0.4356654 , -0.43565632, -0.43565983, -0.4356505 ]),\n",
       " 'split0_train_score': array([-0.69314718, -0.63812602, -0.66318897, -0.52079353, -0.47527695,\n",
       "        -0.46892587, -0.46523206, -0.46513617, -0.46507333, -0.46507194,\n",
       "        -0.46507126, -0.46507125, -0.46507124, -0.46507124]),\n",
       " 'split1_test_score': array([-0.69314718, -0.64055836, -0.66451976, -0.52565942, -0.4611484 ,\n",
       "        -0.46000204, -0.44880171, -0.44835583, -0.44718329, -0.44713165,\n",
       "        -0.44701277, -0.44700911, -0.44699924, -0.44699685]),\n",
       " 'split1_train_score': array([-0.69314718, -0.634844  , -0.66322499, -0.51538033, -0.47106813,\n",
       "        -0.46575272, -0.46239693, -0.46226668, -0.46221011, -0.46220865,\n",
       "        -0.46220805, -0.46220803, -0.46220802, -0.46220802]),\n",
       " 'split2_test_score': array([-0.69314718, -0.63459968, -0.66788296, -0.51151681, -0.46870849,\n",
       "        -0.46864439, -0.47237533, -0.47335425, -0.47466198, -0.47476847,\n",
       "        -0.47490481, -0.47492688, -0.47494666, -0.4749429 ]),\n",
       " 'split2_train_score': array([-0.69314718, -0.63765116, -0.6718277 , -0.51667969, -0.46490697,\n",
       "        -0.45880548, -0.45407526, -0.45393518, -0.45384733, -0.45384595,\n",
       "        -0.45384498, -0.45384496, -0.45384495, -0.45384495]),\n",
       " 'split3_test_score': array([-0.69314718, -0.63795295, -0.66591059, -0.52915092, -0.50091337,\n",
       "        -0.50583355, -0.51757119, -0.51915928, -0.52177118, -0.52194465,\n",
       "        -0.52223268, -0.52224959, -0.5222719 , -0.52228037]),\n",
       " 'split3_train_score': array([-0.69314718, -0.63580648, -0.6682447 , -0.51018399, -0.45560021,\n",
       "        -0.4491737 , -0.44396764, -0.44383879, -0.4437396 , -0.44373827,\n",
       "        -0.44373718, -0.44373716, -0.44373715, -0.44373715]),\n",
       " 'split4_test_score': array([-0.69314718, -0.63986143, -0.66404988, -0.52933221, -0.48796605,\n",
       "        -0.4852537 , -0.48489684, -0.48488565, -0.48536357, -0.48537032,\n",
       "        -0.4854227 , -0.48542815, -0.48543378, -0.48543404]),\n",
       " 'split4_train_score': array([-0.69314718, -0.6349573 , -0.65883551, -0.51200022, -0.46149735,\n",
       "        -0.45701538, -0.45286395, -0.45277505, -0.45270176, -0.4527009 ,\n",
       "        -0.4527001 , -0.45270009, -0.45270008, -0.45270008]),\n",
       " 'std_fit_time': array([4.00519438e-04, 6.57274664e-07, 3.81469727e-07, 1.85606512e-03,\n",
       "        4.90564080e-04, 6.33013237e-04, 4.90388074e-04, 4.00067029e-04,\n",
       "        4.90485456e-04, 8.00752732e-04, 4.90759983e-04, 4.90290904e-04,\n",
       "        4.90213006e-04, 3.81469727e-07]),\n",
       " 'std_score_time': array([4.90524783e-04, 4.00448328e-04, 4.00448158e-04, 4.01783074e-04,\n",
       "        1.09632360e-03, 4.00066972e-04, 3.99971150e-04, 9.34406182e-07,\n",
       "        6.33163825e-04, 7.48978272e-04, 1.91329981e-06, 4.00138139e-04,\n",
       "        4.00209555e-04, 9.53674316e-08]),\n",
       " 'std_test_score': array([0.        , 0.00245541, 0.00134398, 0.00935943, 0.01850039,\n",
       "        0.02120231, 0.02834372, 0.02905263, 0.03027215, 0.03036116,\n",
       "        0.03049224, 0.03050127, 0.03050999, 0.03051539]),\n",
       " 'std_train_score': array([0.        , 0.00136555, 0.0045068 , 0.00370738, 0.00694013,\n",
       "        0.00693235, 0.00753711, 0.00754269, 0.00755701, 0.00755694,\n",
       "        0.0075571 , 0.0075571 , 0.0075571 , 0.0075571 ])}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.47194207846846414\n",
      "{'C': 1, 'penalty': 'l1'}\n"
     ]
    }
   ],
   "source": [
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x903d1fac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CV误差曲线\n",
    "test_means = -grid.cv_results_['mean_test_score']\n",
    "test_stds = grid.cv_results_['std_test_score']\n",
    "train_means = -grid.cv_results_['mean_train_score']\n",
    "train_stds = grid.cv_results_['std_train_score']\n",
    "\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    plt.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    plt.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'logloss' )    \n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用网格搜索自动选取了L1正则，C为1，结果比默认参数略微有一些提高，但是测试数据中错分成类别0的数据多了一条。尝试调整class_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV(cv=5, error_score='raise',\n",
      "       estimator=LogisticRegression(C=1.0, class_weight={0: 0.4, 1: 0.6}, dual=False,\n",
      "          fit_intercept=True, intercept_scaling=1, max_iter=100,\n",
      "          multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n",
      "          solver='liblinear', tol=0.0001, verbose=0, warm_start=False),\n",
      "       fit_params=None, iid=True, n_jobs=1,\n",
      "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
      "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
      "       scoring='neg_log_loss', verbose=0)\n",
      "Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.82      0.82      0.82       102\n",
      "          1       0.65      0.65      0.65        52\n",
      "\n",
      "avg / total       0.77      0.77      0.77       154\n",
      "\n",
      "Confusion Matrix:\n",
      " [[84 18]\n",
      " [18 34]]\n",
      "0.4843007564526005\n",
      "{'C': 1, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "# 网格搜索\n",
    "penaltys_cw = ['l1','l2']\n",
    "Cs_cw = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "\n",
    "parameters_cw = dict(penalty = penaltys_cw, C = Cs_cw)\n",
    "\n",
    "# lr_grid_cw = LogisticRegression(class_weight = 'balanced')# 0.5001226086237056 81 38\n",
    "lr_grid_cw = LogisticRegression(class_weight = {0:0.4,1:0.6})# 0.4843007564526005  84  34\n",
    "grid_cw = GridSearchCV(lr_grid_cw, parameters_cw, cv = 5, scoring ='neg_log_loss')\n",
    "\n",
    "grid_cw.fit(X_train, y_train)\n",
    "lr_grid_predict_cw = grid_cw.predict(X_test)\n",
    "print(grid_cw)\n",
    "print(\"Classification report:\\n\",classification_report(y_test,lr_grid_predict_cw))\n",
    "print(\"Confusion Matrix:\\n\",confusion_matrix(y_test, lr_grid_predict_cw))\n",
    "\n",
    "print(-grid_cw.best_score_)\n",
    "print(grid_cw.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过修改class_weight后训练集上的分数降低了一些，但是显著提高了测试集中类别为1的数据召回率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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X+kkmyyiqolb+r4/jqFKBvrNPM2Wt50cdFCWpeTuXvcNocPonjpwrvTWxlLrWjJ4zPHK/orTy6HMWxpj5xpi6xpjaxphXXevGGGPmuN4bY8wzxpiGxpjGxpiZrvWrXMvRrp//9mScubH5+lJjwqtUPAtJn37BllNFe2PJo+xepNe/ly62jcyP2Wt1NEqpYkqf4M6jwLZtCbirJ/etdvDB7BdJTU+1OiS3CW4xAH9JIX5jKRwirJRyC00W+VB51Gjsfv50+/oAM7aVolIgVVtzybc8zS8sZdfxohlZoZQqWTRZ5INXeDiVnnuexgcNWz+fwv7z+60OyT1sNmxRfehoi+XHmNyffFVK3Xw0WeRTaP/+eDVuxEOL05i0+O84TOmo2urfbAA+ks6l2Nk4HDopkir9Dj48iIMPD7I6jBJDk0U+ic1G1YmvEJwkRH29iW/3fGt1SO5R+VYuBVShffJyNhzSSZGUyq+iLlE+a9Ys3nzzTbfFnxtPlvsotfzq16fcI4PoNuNTXp31Bh2f6kj5gPK5b1icieAdfT/tVk3m9fXbuK3G7VZHpFSJlFGivE6dOhw9epTmzZvTo0cPQkNDM9tMmTIFgAMHDtCrV68bPrSXlpaGl1f2X9N9+vRxf/A50J5FAUUMG4ZERvDwvEtMWvWq1eG4hU/0A3iJA7NjDilppePymlJFrbAlytu3b8/o0aO5/fbb+fDDD5k9ezatWrWiWbNmdO/enZMnnfVWp0+fzogRIwB46KGHGD58OG3btqVWrVqZ5ULcSXsWBWQLDKTymLGYocPw+24xi+sspmv1rlaHVTiRjbgUUpuu535lxe5TdG0YaXVESuXb8X/8g+SduQ/USPrN2SYv9y18G9SnwqhR+Y6lICXKwVmIcMUKZ3mhs2fP0rt3b0SEqVOn8vbbb/P6669ft83JkydZuXIlW7dupX///m7veWjPohCCu3QhsNMdDPjFMHXhBBJSEqwOqXBE8GvWn5a231gWs9nqaJQq0QpSojzDwIEDM98fOnSI7t2707hxY9555x22b9+e7Tb33XcfIkKTJk04cuRIoWLPjvYsCqni3//OxbtWc+8Pp3mv8XuMaTPG6pAKxd64Hyx/jYC9P3AxuTNBvvqviCpZ8toDyOhRVP/vf9weQ0FLlGcIDAzMfD906FBGjRrFXXfdxeLFi5k0aVK22/j6+ma+d5Z8ci/tWRSSd6VKRD71NC32GA7M/R8xx2OsDqlwwutwuWxDerKKRduPWx2NUiVOYUqUZ+f8+fNUrlwZYwyfffaZGyIsGE0WblB20MN4163DXxYLry0bS3J6stUhFYp/s/7catvLrzEbrA5FqRKnsCXKrzVu3Dj69OlDx44diYy07j6ieKK7YoUWLVqYmBjr/qq/vGkTB/7wR+beBv4jn+CpZk9ZFkuhnT0I7zfh9bSB/OmFyUQE++a+jVIW2rlzJw0aNMjXNp68DFVcZfd7EpENxjnRXI60Z+EmAc2aEda/P3fHwJLF09l9drfVIRVcWHUSI2+ll20187ZcO1+VUqVD9f/+56ZKFIWlycKNyj8zEq/QUB5b6GDCr+NId6RbHVKB+TfrTyPbQdbHrLU6FKVUMaDJwo3sZcpQ4aWXqBWXRvnFm5m5a6bVIRVcw/swCLecXMSB05esjkapXJWWS+qeUtjfjyYLNwvp1YuA1q0ZtEL4dMW7HLt4zOqQCiakIilV2nCPfTWzN7l/zLZS7uTn50d8fLwmjBswxhAfH4+fn1+B96GD6N1MRKgwdgyXe9/LwJ+SmVh7IlO6TEFErA4t33yj7+eWuGfYumkVpmudEnkO6uZQpUoV4uLi8lVW42bj5+dHlSpVCry9JgsP8K1Zk/AhQ2g7ZQpLfl3OgtoLuLPmnVaHlX8N78Ux/zmaJSxh25E+NK5SxuqIlMqWt7c3NWvWtDqMUk0vQ3lIuSGP4V29Ok8u9uatla9xLumc1SHlX2A46TU60tu+hu83xVkdjVLKQposPMTm60vFsWModzqZTsvO8GZM0dWddyfvJvdTVU6yL3YF6TopklI3LU0WHhTYti0h99xDnzWG9etms/roaqtDyr/6d5Nu86Z98nLW7I+3OhqllEU0WXhY5AvP4xUQyLAlPkxYNZ7EtESrQ8of/1C4pRv32Ncwe+Nhq6NRSllEk4WHeYWHU/6Zv1FnXyI11x7mo9iPrA4p3+yN+xEpZzm5fRlJqSX3QUOlVMFpsigCof0fwD86mseWefPNhk/ZEb/D6pDyp25P0u1+dE5fyc+/nbQ6GqWUBTRZFAGx2agwfhx+l9N49Bcvxq0aR5ojzeqw8s43CKl3J7281vLDpkNWR6OUsoAmiyLiV78+ZQcNokNMIulbdvDfHf+1OqR8sTXuR1kSSNy9jPOXU60ORylVxDRZFKGIYUPxqliRkUv8mbrhQw4nlKAbxrd0I907iDtZxY/bSmgJE6VUgWmyKEK2wEAqvDyackcvctd6B+PXjC85tWy8/bA17MVdXuuZu+l3q6NRShUxTRZFLLhLF4K6dOH+X9LZ99saZu+bbXVIeSZR9xPMJfwOLefY+RI2BFgpVSiaLCxQYfQo7HYvRi4P5s11b3A68bTVIeVNrTtI9w2jl201P2zWSZGUuplosrCAd6VKRAx7ijrbz9FwxyXeWPeG1SHljd0be9S99PDaxI8b91sdjVKqCHk0WYhITxHZJSJ7ReTFG7TpLyI7RGS7iHyRZf0jIrLH9XrEk3FaoezDD+Fbrx5PLvVh6a75rIhbYXVIedOoL/4mkQonf2HPiQtWR6OUKiIeSxYiYgemAHcCDYEHRaThNW3qAC8B7YwxjYARrvVlgbFAK6AlMFZEwjwVqxXE25uK48fhd+YSj60PYeKaiVxKLQEz0tVoT3pABL3tq/k+VidFUupm4cmeRUtgrzFmvzEmBZgJ3HtNm8eAKcaYswDGmIzHg3sAPxljzrg++wno6cFYLeHftCmh/fvTftV5/PYfZ/LGyVaHlDubHXtUX7p4xfLTpr0lZzSXUqpQPJksKgNZHySIc63Lqi5QV0RWisgaEemZj21LhfLPjMQrNIwXl4Uyc8cXxJ6MtTqk3EX1w8ek0DBhJRsPnbU6GqVUEfBksshuDs5r/wz1AuoAdwAPAtNFJDSP2yIiQ0QkRkRiSup0ivYyZYh88QXK7j9N3x1BjF89ntT0Yv6EdJXbcIRU4T7v1Xy/SUdFKXUz8GSyiAOqZlmuAlz7zRIHzDbGpBpjfgd24UweedkWY8zHxpgWxpgWERERbg2+KIX06kVAm9bc/3Myp+L28O9t/7Y6pJzZbNii+tJBtvDLlt2kpjusjkgp5WGeTBbrgToiUlNEfICBwJxr2nwPdAIQkXCcl6X2AwuB7iIS5rqx3d21rlQSESqMGYM9JY0X11Tg4y0fs/98MR+aGtUPO+m0Sl7Fr3tKyHMiSqkC81iyMMakAcNwfsnvBL4yxmwXkQki0tvVbCEQLyI7gKXAc8aYeGPMGWAizoSzHpjgWldq+dasSbm//pXa64/Q/KAX41eNx2GK8V/sFaMxYbXo471GR0UpdROQ0jKapUWLFiYmJsbqMArFkZzM773v5WLKRQY/dI4X24+hf73+Vod1Yz+/gmPF29ye/hELX76fQF8vqyNSSuWTiGwwxrTIrZ0+wV2M2Hx9qTBuLD7H4hm2tRLvbniXE5dOWB3WjUX1w4aDTo7V/LSjGMeplCo0TRbFTGCbNoTccw9tlhyn3Klk/rH2H1aHdGPlG2DKN6Sfz1pm66UopUq1fCcLEbGJSIgnglFOkS88j83fn5d/ieDnQ0tYfHCx1SHdkET1panZyZ49u4i/mGx1OEopD8lTshCRL0QkREQCgR3ALhF5zrOh3by8wsMp/8wzlNl2iAEHKvLq2ldJSEmwOqzsNeoLQE9ZzbytOimSUqVVXnsWDY0xCcB9wHygGvCwx6JShPZ/AP/oaPouSCDl7Bne3fCu1SFlr1xtqNSM/n5r+X6TXopSqrTKa7LwFhFvnMlitjEmlWyeqFbuIzYbFSaMRy5cYkxsTb7Z/Q3rj6+3OqzsRfWjbvpeTh/exaH4y1ZHo5TygLwmi2nAASAQWCEi1YFiel2k9PCrV4+yjzxCtaW7aB8fzoTVE0hOL4b3BRr1AaCXbTVzNmvvQqnSKE/Jwhgz2RhT2Rhzl3E6iOvJa+VZEUOfxKtiRYb+ZOfw2d+Ztnma1SFdr0wVqNqaAf7r+D72qFaiVaoUyusN7uGuG9wiIv8WkY1AZw/HpgBbYCAV/v4y9t+P8Nz+BszYNoNdZ3ZZHdb1ovpRPe0Acuo3th/VTqdSpU1eL0P9yXWDuzsQAQwGJnksKnWV4M6dCerSheZz91IjMZDxq8eT7ki3OqyrNbwXIzZ6e63RZy6UKoXymiwySobfBcwwxmwm+zLiykMqjB4FNhsvr4xk66ktfPnbl1aHdLXgSKRGBx7wXcuc2COkO/RSlFKlSV6TxQYRWYQzWSwUkWCgGFe5K328K1UiYtgwAtbt4JH4BkzeNJmjF4vZXBJR/aiQdoTwi7tY+3u81dEopdwor8niz8CLwG3GmMuAD85LUaoIlR30ML7169Nrzgn8kg0T10wsXjeTG9yDsXnR12cts3VSJKVKlbyOhnLgnIDoZRF5C2hrjNni0cjUdcTLi4rjxmJOnmbCzkb8euRX5v8+3+qwrggoi9TuTB/vtczfdpSk1GJ2X0UpVWB5HQ01CRiOs9THDuBpEXnNk4Gp7Pk3bUrogP5EzltPt6RbeH3d65xNKkbzYEf1o2zaCeok72TZrpI51a1S6np5vQx1F9DNGPOJMeYToCdwt+fCUjkpP3Ik9rAwHluYzsWkBN6KeQuAwQsGM3iBxVcH692FsfvygN86HRWlVCmSn6qzoVnel3F3ICrv7GXKEPnii7BjDy+fbMWcfXNYdWSV1WE5+YUgdbvTy76Wpb8dJyEp1eqIlFJukNdk8RqwSUQ+FZHPgA1AMZ5oofQL6XU3gW3b0OjrjURJFSasmUC6KSb3CKL6EZwWTzPHdhZsPW51NEopN8jrDe4vgdbAd65XG2PMTE8GpnImIlQYMwaTnMKotRU5cvFI8RlKW6cHxjuQBwPWM1trRSlVKuSYLETk1owXUBGIAw4DlVzrlIV8atSg3JAheC1ZzdCUdpy4fIJLqZesDgt8ApD6d9GNtazbd4ITCUlWR6SUKiSvXD5/O4fPDFofynLlhjxGwty5dP5qHzMe9OJAwgFS01PxtntbG1ijvvhv/Zq2sp0fNjfmLx1qWRuPUqpQcuxZGGM65fDSRFEM2Hx8qDBuLOmH43g4JpDEtEReXfuq9Q/r3dIFfMswKDiG73VUlFIlXm49CwBEpG82q88DW40xJ90bksqvwNatCel9D53n/MBpfPiWb6kdWpuHG1o4maGXLzS4hw5bv+fJI6fZe/Iit5QPsi4epVSh5Kfcx3Tgj67Xv4BngJUiotOrFgORzz+PsUHvVel0rdqFt2Le4pe4X6wNKqovPukXucO+mTnau1CqRMtrsnAADYwx/Ywx/YCGQDLQCnjBU8GpvPMKD+dsOV/8k9J5YX8D6oXV47kVz7H37F7rgqrZEQLK8WjIRp0USakSLq/JooYx5kSW5ZNAXWPMGUCfuiomLgV7cznQi7PvfsA7YY/hZ/dj2M/DrCsHYveChvdxW8o6Tp05w6bD56yJQylVaHlNFr+IyFwReUREHgHm4JyLOxDQb4DiQoT48n74VKlC4ksTmdx0Aqcun2LkspGkpluU06P64ZWeSA/vWGZv0ktRSpVUeU0WQ4EZQFOgGfAZMNQYc8kYo3NxFxM95q2j+4/rqTz5fdITEijz2gwmtB7HhhMbeGXtK9ZcBqrWBoIr8mjIBuZuOUZauk6DolRJlNcnuA3wK/AzsBhYYfQCdLHlV68eFcaO5fKaNdw2bx9Dmgzhuz3f8d8d/y36YGw2aNSHJonrSLl0jl/3ni76GJRShZbXEuX9gXXA/UB/YK2I3O/JwFThhPbtQ5n7+xE/dRqPnIuiW/VuvL3hbVbErSj6YKL6YXOkcq/fRmbHFpOSJEqpfMnrZajROGfJe8QYMwhoCfzdc2Epd6jw8sv4NmjAsReyWUG/AAAgAElEQVRfYlzNodQLq8fzK54v+hFSlZtDaDUeDtrAwu3HuZySVrTHV0oVWl6The2ah+/i87GtsojNz48q778HDgfxf3uR99u/hb+Xf9GPkBKBqH7UvRSDb8pZFu/U5ziVKmny+oW/QEQWisijIvIoMA/IdT5PEekpIrtEZK+IvJjN54+KyCkRiXW9/pLls/Qs6+fk9YTU1XyqVaPSa/8gads25INPeb/T+9aMkIrqh5h0BgZt0lFRSpVAeb3B/RzwMdAEiAY+Nsbk+DCeiNiBKcCdOB/ie1BEGmbT9H/GmKau1/Qs6xOzrO+dlzhV9oK7dqXsn//E2S++pPqaQ0xsN7HoR0hFRkF4XQb6r2f57lOcuZRSNMdVSrlFni8lGWO+NcY8Y4wZaYyZlYdNWgJ7jTH7jTEpwEzg3oIGqgqn/IgR+LdozrGxY+niqFv0I6Rcl6KqXdhEmOMM87YeK5rjKqXcIrf5LC6ISEI2rwsikpDLvivjnPsiQ5xr3bX6icgWEflGRKpmWe8nIjEiskZE7rtBfENcbWJOnTqVSzg3N/H2pvLb72Dz9ydu+AieqPNo0Y+QatQXwfBomVitFaVUCZNbifJgY0xINq9gY0xILvuW7HZ5zfIPOEuJNMH5/MZnWT6rZoxpAfwBeE9EamcT38fGmBbGmBYRERG5hKO8I8tT+e23Sfn9d06MGcfEthOLdoRURF2IbExf37WsP3CWuLOXPX9MpZRbeHJEUxyQtadQBbhqkL0xJt4Yk+xa/BfQPMtnR10/9wPLcD45rgopsHUrIoYPJ2H+fJK/mc3kzpOLdoRUVF8qJmyhipzSZy6UKkE8mSzWA3VEpKaI+AADcdaUyiQiFbMs9gZ2utaHiYiv63040A7Y4cFYbyrlHvsLQR07cuK1SZTZd5LJnSZzOvF00YyQinJOjTKk3GZmxx7RSrRKlRAeSxbGmDRgGLAQZxL4yhizXUQmiEjG6KanRWS7iGwGngYeda1vAMS41i8FJhljNFm4idhsVHp9Et7lyxM3YgQNvKowoe0ENpzYwMQ1Ez37BR5WAyq34G5Zxe4TF9l57ILnjqWUcps8zZRXUMaY+VzzPIYxZkyW9y8BL2Wz3SqgsSdju9nZQ0Op/N57HPzDHzj6wgvcOXUq+8/vZ9qWadQOrc0jjR7x3MGj+lFu4UvcYjvO7M1HaFgpt9tfSimr6VPYNzH/xlFEjh7FpRW/EP/xxzzZ9Em6Ve/GOxve8ewIqUb3AcLQ8lv4IfYoDodeilKquNNkcZMLHTCAkHvu4dTkD0hcs5ZX2r3i+RFSIZWgeju6pq/g6PlE1h0445njKKXcRpPFTU5EqDh+HD61anLkb8/ifebCVSOkziR56Is8qg/BF/YT7XOU2frMhVLFniYLhS0ggCqTJ+NISuLIyGeI9Cl3ZYTUUg+NkGpwL4idoRGbmb/1OMlp6e4/hlLKbTRZKAB8a9Wi4sQJJG7cyMm336FxRGMmtpvIxpMbPTNCKigCanWkQ/JyziemsHyXPoGvVHGmyUJlKnP33YT98Y+c+fRTEhYt4s6ad/LXJn9l1t5Z/GfHf9x/wKh++F88TPuAw/qAnlLFnCYLdZXyLzyPX5MmHBs1mpQDBzJHSL0d44EaUvV7gc2bJ8rFsnjnCS4kFWHJdKVUvmiyUFex+fhQ5b13EbuduOEjIDmFV9u/Sv2y9Xl+xfPsObvHfQfzD4U63bjt0nJS0tJYuP2E+/atlHIrTRbqOt6VKlHprTdJ3r2b4xMm4u/lz+TOkwnwCuCpn59y7wipqH74XD7GnWUO6qgopYoxTRYqW0EdOhD+xOOc/+47zn37LRUCK/B+p/fdP0Kqbk/w8uex0FhW7j3NyQtJ7tmvUsqtNFmoGwofOpTAtm04PmEiSTt3XjVCasKaCe4ZIeUbBHV70DhhKWLS+WGzToqkVHGkyULdkNjtVHrrLeyhocQNH0F6QgJ31ryTx6Mf5/u937tvhFRUP7wSTzMw4mDmpEgDpq1mwLTV7tm/UqrQNFmoHHmVLUvld98h9ehRjo4ahTGGJ6KfyBwhtfzw8sIfpE438Anm4aAYNsed5/fTlwq/T6WUW2myULkKuPVWyj/7Ny4uXsKZGZ9iE5t7R0h5+0P9u6l7Zik+ksb3m/RGt1LFjSYLlSdlH3mE4O7dOfn221zesCFzhFSgd6B7RkhF9cOWfJ6/VDygkyIpVQxpslB5IiJU/Mer+FSpwpERI0k7fZoKgRWY3PlKDamU9JSCH6DWHeAfRn//dRyIv8ylFK0VpVRxoslC5Zk9KIjKk98n/cIFjvztWUx6OlHhUbzS7pXC15Dy8oEGval+ainB9jTiLybnvo1SqshoslD54levHhXGjuXy2rWc+uADAHrW7OmeEVJRfZGUSwytso/4Syl6KUqpYkSThcq30D73EfrA/cRPncaFZcsAeCL6CbpX7164EVI1OkBgee71WkNquuF8Ypr7glZKFYomC1UgkaNH49ugAUdfeJGUuCPYxMYr7V+hQbkGBR8hZbNDo/uocGI5IbYkTl1M1t6FUsWEJgtVIDY/P6q8/x44HBwZMQJHSopzhFSnQo6QiuqHpCXxrNdXnLmUQt+PVhGj064qZTlNFqrAfKpVo9Kk10jato2TkyYBEBkYWbgRUlVaQkgV7rOvZLjfPI6eS+T+qav5639j2H/qogfOQuXH4AWDGbxgsNVhFFqrGf1oNaOf1WG4RVGdiyYLVSjBXbpQ9s9/4uwXX3L+h7kAV42QmrA6nzWkbDaI6kOQuUhP700sffYO/tatLr/uOU23d1fw9++3cVpHSllmx7EEdhxLsDoMZQFNFqrQyo8ciX+L5hwbM4bkvXsB5wipJ6KfYPa+2Xy2/bP87TCqHzYMIY7zBPh48VSXOix7rhN/aFmNL9Yd4o43l/Hhz3tI1GcxlCoymixUoYmXF5XfeQdbYCBxTw/HcclZ2+nx6MfpXr0772x4J38jpCo2JRkfwhxnIMn5V2xEsC8T74ti0cjbaVu7HG8t2s0dby3lq/WHSXcU75vgpeXSjbq5abJQbuFdvjyV33qLlAMHOPb3MRhjrhshtfvs7rztTIRPQkPwM4nwUTv4/ZfMj2pHBPHxoBZ89dc2VCzjz/PfbuHuyb+wbNfJYjtySi/dqNJAk4Vym8DWrYgYPpyE+fM5+8UXAFeNkHr656eJT4zP076WBgQzJrwi2L3gs16wYBSkJmZ+3rJmWWY92ZYpf7iVyynpPDpjPQ//ex3bj573yLkpdbPTZKHcqtxjfyHojjs4Mel1ErdsAa4ZIbUs7yOkdvv4weO/wm2PwZopMK0jHNmY+bmIcHeTiix+piNjejVk29Hz9PrgV575XyxHziXmsGelVH5pslBuJTYblSa9hnf58sSNGEHa2bOAa4RU+1fYdHJT/kZI+QTC3W/Bw7Mg+QJM7wpLX4Ms07r6eNn4U/uaLH+uE0Nur8Xcrcfo9NYyJv34GwlJbpr+VambnCYL5Xb20FAqv/ce6adOc/SFFzAOBwA9axRihFTtzvDkamj8ACyf5EwaJ3+7qkkZf29eurMBS5+9g16NKzJtxT46vrGUT379nZQ0h7tOT6mbkiYL5RH+jaOIHD2KSyt+IX7atMz1j0c/To8aPXhnwzssO7zshtsHVmtGYLVm1+w0FPpOg/7/hfOHYdrtsOpDcFydCCqH+vPOgKb8MKw9DSuFMGHuDrq9u5x5W44V25vgShV3miyUx4QOGEBI73s4NfkDLq1aBYBNbExsN5GG5RrywooX8j5CKquGveHJNXBLV1g02nkD/OyB65pFVS7D//25FZ8Ovg0/LztDv9io5UOUKiCPJgsR6Skiu0Rkr4i8mM3nj4rIKRGJdb3+kuWzR0Rkj+v1iCfjVJ4hIlQcNw7fW2pz5NnnSD1xAnCOkHq/0/vOGlJLnsrzCKmrBJWHgZ/Dvf+EY1ucQ2w3fAbX9BxEhDvqlWf+8A680a/JVeVD9mn5EKXyzGPJQkTswBTgTqAh8KCINMym6f+MMU1dr+mubcsCY4FWQEtgrIiEeSpW5Tm2gAAqv/8+jqQkjowYiUl13nCODIzkg84fEJ8Un68RUlcRgWZ/hCdXQaVm8MPT8MUAuHD8uqZ2m9D/tqosffYOnu3uLB/SXcuHKJVnnuxZtAT2GmP2G2NSgJnAvXnctgfwkzHmjDHmLPAT0NNDcSoP861Vi0qvTCRx0yZOvv1O5vpG4Y0yR0iNXz2+4PcTQqvBoDnQ83X4fTn8szVs+y7bpgE+XgzrXIflz2v5EKXyw5PJojJwOMtynGvdtfqJyBYR+UZEquZnWxEZIiIxIhJz6tQpd8WtPCDkrrsIe+ghznz6KQmLFmWu71mjJ09GP8mcfXP4dPunBT+AzQatH4e//gJhNeGbwfDNn+By9vcnwoNKbvkQpazgyWQh2ay79r/CH4AaxpgmwGIgYzxlXrbFGPOxMaaFMaZFREREoYJVnhf5/HP4RTfh2KjRpBw4kLk+Y4TUuxvezXGEVJ5E1IU//wSdXoYds+GfbWDPTzdsnlE+5OvHS075EKWs4MlkEQdUzbJcBTiatYExJt4Yk3HB+F9A87xuq0oe8fGhyrvvInY7ccNH4Eh0PmUtIoUfIZWV3Qs6Pgd/WQL+YfD5/fDDcEi+8Q3t22po+RClcuLJZLEeqCMiNUXEBxgIzMnaQEQqZlnsDex0vV8IdBeRMNeN7e6udaqE865UiUpvvUny7t0cn/hK5np/L38md55MkHcQTy15ilSHG568rtQUhiyDtk87R0pNbQcHV9+wuZYPUerGPJYsjDFpwDCcX/I7ga+MMdtFZIKI9HY1e1pEtovIZuBp4FHXtmeAiTgTznpggmudKgWCOnQg/IknOP/dd5z75pvM9eUDyjO582TOJJ1h37l9OIwbnrr29oPuE2HwfOew2hl3wqK/Q2rSDTfR8iFKXc+jz1kYY+YbY+oaY2obY151rRtjjJnjev+SMaaRMSbaGNPJGPNblm0/Mcbc4nrN8GScquiFD32SwLZtOD5hIkk7d2aubxTeiIntJ3Ix9SK7z+7mo9iPmLd/HltPbeV8ciEuCVVvC0+shOaPwKrJ8PEdcGxzjpto+RClrvCyOgB1cxK7nUpvvcXvffoSN3wENb/5GntICOAcITV542ROXDrBR5s/wmQZ21DGtwzVg6tTNaQq1YKrUS2kGtWCq1E9pDplfMvkfFDfYLjnfajfC2YPg391ho4vQvuRzvscN5BRPuRP7Wvy2o87mTB3B5+tPsDzPepzV+MKiGQ3HkOp0kWThbKMV9myVH73XQ4OGsTRUaOo8sEHmV+8kQGRRAZEMrXbVI5cOMLBhIMcunCIQwmHOHThEJtObGL+/vlXJZIQnxCqh1SnanDVq35WC65GqF/olQPX6eYsSjj/OVj6Cuz+EfpMg/A6OcabUT5k+e5TvDb/N4Z+sZFm1UIZfVcDWtQo65HfkVLFhSYLZamAW5sR+dyznHhtEmc+mUG5P//pqs997b7UCq1FrdBa122bkp5C3IU4Dl04xMGEgxy+cJhDCYfYfGozP/7+43WJpFpwNaqGXEkg1To+TbVb7iB04d+Rqe2h63hoOcT5zMYNZJQP6VAngm83xPH2T7u4f+pqejSK5Pme9akdEeS+X45SxYgmC2W5sEGDuLxhIyffeQf/6CYEtGjBwA+2Oz/M4bl9H7tPzonkYhyHEw5f1SvZcmoLCw8svOrmeXDVilRLTaPahjeo9tv/Ub35X6ga2ZRqIdUI8w3L9jJTRvmQXtEV+eTX3/lo2T4W71zBH1pWY3jXOoQH+Rb696JUcaLJQllORKj4j1dJ7reLIyOfoeas7Et15IeP3YdaZWpRq0z2ieTIxSOZl7QOJhzkcMJhtpzezsKUczg2vp3ZNtg72Nkbcd0nyeyVuBJJRvmQgS2r8f7iPXyx7hCzNh3h8Y61+HP7Wvj72At9LkoVB5osVLFgDwqi8uTJHBgwgCN/e9Y5zNVDN4597D7ULFOTmmVqXvdZavwejsx5kkMnt3CochMOVm3F4aTTbD29lYUHr+6RBHkHZd5grxpclRZR1WnVIJxv1yTz1qJd/HfNQf7WrR7GCCL6NLgq2aS0lDRo0aKFiYmJsToMVUjnZn3PsZde4nyoD+fL+dJj3rqiD8LhgDX/hCUTwDcIer0LDe8lNT3V2SPJcqM94+fRi0dJN1cKEfrbA0lPKcelS6GQHojdK5FmFZxFlw3GWUrEGOf7K2szS4w41zqXr/ocg/P/JrNVxn5utHxlX1f2TDbtsx7J2fiqNYDh+EVnDbbIoHDXmV7//XH9V8q1K0yOn2a3Jr/bmGz3ccWZxLMYoJx/aI7t3MLDX7HxSecQRwDbn/yqQNuLyAZjTIvc2mnPQhUroX3uI3HjBvj6G1J9bRiHA8nhhrNH2GzQdphzcqVZQ+CrQdBkAN53vkGNMjWoUabGdZukOlI5evHoVTfaDyYc5Lf43zmdtBURw+ZLsfmLw2T0rLL5aTKWc2jjei/X7Sf7beWG+5MrLbzTAOFs2oVr9nW1vKzJ+fNs2l/zpXv9vaRrlrP9kna1sSUjwKX0onlexpODq8WWAsbbg0dwHUd7Fqq4cSQns/W2W/FJcWALCsK/SRP8mzXDv2lT/KObZD6PUSTSU2HFW7DiTQiKhPumOOcDz4eWM/oChhUPf4mIkPE/AITM5czPivFzG61m9ANg7eBvLY6kcErLeUDhz0V7FqrEsvn6cqJSAAGX0qjd+W4SY2M5/dFHzstDIvjUroV/06YENG2Kf9Om+NSq5bneh90bOr0EdbvDrMfhv33gtr9AtwngE5inXWT8xe7n5eeZGJUqAposVLFk7MKlEG8qjh8HQPrFSyRt3cLlTZtIjI3lwk+LOf+N8y8pW0gI/tHR+DeNdvU+orEHufl5h8rN4a8rYMlE5/2MfT/DfVOhWiv3HkepYkqThSoR7EGBBLZpQ2CbNgAYh4OUAwdI3BRLYmwsibGbOP3hr5mjqHxvucWZOFyXr3xq1ij85R1vf+j5D6h3J3z/JMzoCe2Gwx0vgZc+V6FKN00WqkQSmw3fWrXwrVWL0H59AUi/cIHEzVtcySOWhAULOPf11wDYy5TBr2l05qUrv8ZNsAfl7TLSdWp2cBYlXPgS/Pquc3KlPtOgQpS7Tk+pYkeThSo17MHBBLVvR1D7doCr97F/P4mxsa7LV5s5tXyFs7HNhm/dupmXrgKaNsW7evW89z78QuDeKc6ihHOedlax7fQStB1+XVHC6qn73HiWSllDR0OpYmnwgsEAzOjp3ur06efPk7hly5XLV5s347h0CQB7WJjr3oez9+HfOApbYB56H5fiYd5I5zSuVVpCn6lQrnbmx/O7Op+vuGvxDreeixVKy7mUlvOAwp+LjoZSKhv2MmUI6tCBoA4dADDp6STv3Zd56SoxNpaLy5a5GtvxrVc389KVf9OmeFeten3vI7AcPPAZbP0G5v8NprZ3jpZq8eccixIqVZJoslDFkrt7FDcidjt+9eriV68uYQP6A5B29ixJW7ZkXro6//1szn7xJQD2cuVcicN5/8MvKgqbv7+zNEmTB6BGO+dcGfOfhd/mOS9VKVUKaLJQ6hpeYWEEdexIUMeOgKv3sWePs+fhunx1cckSV2Mv/OrVu3LpqlkzvP/4DbJhBix6Gf7ZhkoRNo6e8rfwjJQqPE0WSuVC7Hb86tfHr359wgYOBCDtzBkSYzdnXro69913nP38cwDsEeHOS1d1RuJ/dh5NasfSqPY5ePMW8PK78vLOeO+bw3r/K597+13dzsvXOZz32u0z1tt9PFaMUd18NFkoVQBeZcsS3LkTwZ07AWDS0kjevTvz0lXGg4MARioiXgavAH9neQ9JQ+QiyAVnNVoxCAbE4apO60BwIKSDGOc2NuP83hdc27jygBjEdmV9Zhubq43dC+x2509vb8TuhXh5gZdPlp/e4J3x0xfx8nGu9/F1Lrte+PhR0TcJA1z+7sMriSjjoM4F53JmjpLrEtaVp+0z2mXZ3ibZbJflOFcdE8B2VRPEdtXxhGtice0/zJ4CQNIvc3L6x1x0CpHUQ+2ppJuCb59XOhpKKQ9JO32axM2b2fbiMGwOqHpnP0hLxzjSXT8dkJ6GSXdAejomPR0c6ZiMNukOTFoapKe6fqY526Q5f5r0NGebq/bncP50ODAOA47S8d+3ypl3aCq3rNlboG11NJRSFvMKDye4SxfOlnH+1dfqlVeKPAZjjLOmlisZmXSHMyGlp19JUOmuRJOWhnFkJKh0TEoSpFzGpCRDSiImNZFtb7wEQNTwvzv373CWMr9yQK4sG5OlXnmWdZk/s1t35TOTdZ2rNHtGaXUgSyI0WX5kPXaWZcxV+9/75ccA1H7wsTz+Jm+gGPyxvf9/07no0bq2TposlCrFRATsduelKDfsL/6NMQAE3jPIDXuzzrH/+wyAZo++aHEkhXf08/8WyXF0ELhSSqlcabJQSimVK70MpZSHJYs+Y6FKPk0WSnnYpD/UAKCPtWEoVSiaLJRSeTb+jw0AuMviOAqrtJwHFN256D0LpZRSudJkoZRSKleaLJRSSuVK71ko5WENK4ZYHYJShebRnoWI9BSRXSKyV0Ru+KikiNwvIkZEWriWa4hIoojEul5TPRmnUp40o+eMIpufQylP8VjPQkTswBSgGxAHrBeROcaYHde0CwaeBtZes4t9xpimnopPKaVU3nnyMlRLYK8xZj+AiMwE7gWunSh2IvAG8KwHY1FKucHawd9aHYJblJbzgKI7F09ehqoMHM6yHOdal0lEmgFVjTFzs9m+pohsEpHlItIhuwOIyBARiRGRmFOnTrktcKWUUlfzZLLIrshlZj1fEbEB7wJ/y6bdMaCaMaYZ8AzwhYhcd5fQGPOxMaaFMaZFRESEm8JWSil1LU8mizigapblKsDRLMvBQBSwTEQOAK2BOSLSwhiTbIyJBzDGbAD2AXU9GKtSSqkceDJZrAfqiEhNEfEBBgKZcxgaY84bY8KNMTWMMTWANUBvY0yMiES4bpAjIrWAOsB+D8aqlFIqBx67wW2MSRORYcBCwA58YozZLiITgBhjTE6T394OTBCRNCAdeNwYc8ZTsSqllMqZzsGtlFI3sbzOwa3lPpRSSuVKk4VSSqlcabJQSimVq1Jzz0JETgEHC7GLcOC0m8KxUmk5D9BzKa5Ky7mUlvOAwp1LdWNMrg+qlZpkUVgiEpOXmzzFXWk5D9BzKa5Ky7mUlvOAojkXvQyllFIqV5oslFJK5UqTxRUfWx2Am5SW8wA9l+KqtJxLaTkPKIJz0XsWSimlcqU9C6WUUrnSZOEiIhNFZItrGtdFIlLJ6pgKSkTeFJHfXOczS0RCrY6poETkARHZLiKOjGl3S5K8Ti1cEojIJyJyUkS2WR1LYYhIVRFZKiI7Xf9uDbc6poISET8RWScim13nMt5jx9LLUE4iEmKMSXC9fxpoaIx53OKwCkREugM/u4o5vg5gjHnB4rAKREQaAA5gGvCsMabEFABzVU7eTZaphYEHr51auKQQkduBi8B/jDFRVsdTUCJSEahojNnomtZ5A3BfSfznIiICBBpjLoqIN/ArMNwYs8bdx9KehUtGonAJJMtETSWNMWaRMSbNtbgG51wiJZIxZqcxZpfVcRRQ5tTCxpgUIGNq4RLJGLMCKPHVn40xx4wxG13vLwA7uWYWz5LCOF10LXq7Xh757tJkkYWIvCoih4E/AmOsjsdN/gT8aHUQN6lcpxZW1hKRGkAzYK21kRSciNhFJBY4CfxkjPHIudxUyUJEFovItmxe9wIYY0YbY6oCnwPDrI02Z7mdi6vNaCAN5/kUW3k5lxIqx6mFlbVEJAj4FhhxzZWFEsUYk26MaYrzCkJLEfHIJUKPTX5UHBljuuax6RfAPGCsB8MplNzORUQeAXoBXUwxvzGVj38uJU1uUwsri7iu738LfG6M+c7qeNzBGHNORJYBPQG3D0K4qXoWORGROlkWewO/WRVLYYlIT+AFnNPUXrY6nptYjlMLK2u4bgr/G9hpjHnH6ngKwzUFdajrvT/QFQ99d+loKBcR+Raoh3PkzUGcU7kesTaqghGRvYAvEO9ataYEj+zqA3wARADngFhjTA9ro8o7EbkLeI8rUwu/anFIBSYiXwJ34KxwegIYa4z5t6VBFYCItAd+Abbi/O8dYJQxZr51URWMiDQBPsP575cN+MoYM8Ejx9JkoZRSKjd6GUoppVSuNFkopZTKlSYLpZRSudJkoZRSKleaLJRSSuVKk4VS+SAiF3NvleP234hILdf7IBGZJiL7XBVDV4hIKxHxcb2/qR6aVcWbJgulioiINALsxpj9rlXTcRbmq2OMaQQ8CoS7ig4uAQZYEqhS2dBkoVQBiNObrhpWW0VkgGu9TUT+6eopzBWR+SJyv2uzPwKzXe1qA62Al40xDgBXddp5rrbfu9orVSxoN1epgukLNAWicT7RvF5EVgDtgBpAY6A8zvLXn7i2aQd86XrfCOfT6Ok32P824DaPRK5UAWjPQqmCaQ986ar4eQJYjvPLvT3wtTHGYYw5DizNsk1F4FRedu5KIimuyXmUspwmC6UKJrvy4zmtB0gE/FzvtwPRIpLTf4O+QFIBYlPK7TRZKFUwK4ABrolnIoDbgXU4p7Xs57p3EYmz8F6GncAtAMaYfUAMMN5VBRURqZMxh4eIlANOGWNSi+qElMqJJgulCmYWsAXYDPwMPO+67PQtznkstuGcN3wtcN61zTyuTh5/ASoAe0VkK/Avrsx30QkocVVQVemlVWeVcjMRCTLGXHT1DtYB7Ywxx13zDSx1Ld/oxnbGPr4DXirB84+rUkZHQynlfnNdE9L4ABNdPQ6MMYkiMhbnPNyHbrSxa6Kk7zVRqOJEexZKKaVypfcslFJK5UqThVJKqQsbbuMAAAAiSURBVFxpslBKKZUrTRZKKaVypclCKaVUrjRZKKWUytX/A3xxe7eWdkyJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x903faf82e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CV误差曲线\n",
    "test_means = -grid_cw.cv_results_['mean_test_score']\n",
    "test_stds = grid_cw.cv_results_['std_test_score']\n",
    "train_means = -grid_cw.cv_results_['mean_train_score']\n",
    "train_stds = grid_cw.cv_results_['std_train_score']\n",
    "\n",
    "n_Cs = len(Cs_cw)\n",
    "number_penaltys = len(penaltys_cw)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs_cw)\n",
    "for i, value in enumerate(penaltys_cw):\n",
    "    plt.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys_cw[i] +' Test')\n",
    "    plt.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys_cw[i] +' Train')\n",
    "\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'logloss' )    \n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.1 default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for classifier LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0):\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.77      0.88      0.82       102\n",
      "          1       0.68      0.48      0.56        52\n",
      "\n",
      "avg / total       0.74      0.75      0.73       154\n",
      "\n",
      "\n",
      "Confusion matrix:\n",
      "[[90 12]\n",
      " [27 25]]\n"
     ]
    }
   ],
   "source": [
    "SVC1 = LinearSVC().fit(X_train, y_train)\n",
    "\n",
    "y_predict_svc = SVC1.predict(X_test)\n",
    "\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\" % (SVC1, classification_report(y_test, y_predict_svc)))\n",
    "print(\"Confusion matrix:\\n%s\" % confusion_matrix(y_test, y_predict_svc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "默认参数和logistic回归一样，都是L2正则和C=1，效果也差不多。loss使用了squared hinge。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.2 网格搜索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV(cv=5, error_score='raise',\n",
      "       estimator=LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0),\n",
      "       fit_params=None, iid=True, n_jobs=1,\n",
      "       param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
      "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
      "       scoring=None, verbose=0)\n",
      "Classification report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.77      0.88      0.82       102\n",
      "          1       0.68      0.48      0.56        52\n",
      "\n",
      "avg / total       0.74      0.75      0.73       154\n",
      "\n",
      "Confusion Matrix: \n",
      " [[90 12]\n",
      " [27 25]]\n",
      "0.7785016286644951\n",
      "{'C': 0.1}\n"
     ]
    }
   ],
   "source": [
    "Cs_svc = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "parameters_svc = {'C':Cs_svc}\n",
    "SVC2 = LinearSVC()\n",
    "grid_svc = GridSearchCV(SVC2, parameters_svc, cv = 5)\n",
    "\n",
    "grid_svc.fit(X_train, y_train)\n",
    "svc_grid_predict = grid_svc.predict(X_test)\n",
    "\n",
    "print(grid_svc)\n",
    "print(\"Classification report: \\n\", classification_report(y_test, svc_grid_predict))\n",
    "print(\"Confusion Matrix: \\n\", confusion_matrix(y_test, svc_grid_predict))\n",
    "\n",
    "print(grid_svc.best_score_)\n",
    "print(grid_svc.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "网格搜索最后选定的参数和默认参数不同，但是预测结果却是相同的，尝试调整class_weight。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV(cv=5, error_score='raise',\n",
      "       estimator=LinearSVC(C=1.0, class_weight='balanced', dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0),\n",
      "       fit_params=None, iid=True, n_jobs=1,\n",
      "       param_grid={'C': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100]},\n",
      "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
      "       scoring=None, verbose=0)\n",
      "Classification report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.85      0.80      0.82       102\n",
      "          1       0.65      0.71      0.68        52\n",
      "\n",
      "avg / total       0.78      0.77      0.78       154\n",
      "\n",
      "Confusion Matrix: \n",
      " [[82 20]\n",
      " [15 37]]\n",
      "0.758957654723127\n",
      "{'C': 20}\n"
     ]
    }
   ],
   "source": [
    "# Cs_svc = [0.001, 0.01, 0.1, 1, 10, 100, 1000]  # 10  0.758957654723127  81  38\n",
    "Cs_svc = [1, 2, 3, 4, 5, 6, 7, 8, 9,10, 15, 20,\n",
    "          30, 40, 50, 60, 70, 80, 90, 100] # 30 0.7801302931596091  77  35\n",
    "parameters_svc = {'C':Cs_svc}\n",
    "SVC2 = LinearSVC(class_weight = 'balanced')\n",
    "grid_svc = GridSearchCV(SVC2, parameters_svc, cv = 5)\n",
    "\n",
    "grid_svc.fit(X_train, y_train)\n",
    "svc_grid_predict = grid_svc.predict(X_test)\n",
    "\n",
    "print(grid_svc)\n",
    "print(\"Classification report: \\n\", classification_report(y_test, svc_grid_predict))\n",
    "print(\"Confusion Matrix: \\n\", confusion_matrix(y_test, svc_grid_predict))\n",
    "\n",
    "print(grid_svc.best_score_)\n",
    "print(grid_svc.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "直接修改class weight之后，也提高了类别为1的数据召回率，但是得分也下降了。调整C范围之后取了30，发现分数提高，类别1数据召回率下降。再次运行之后C取了20，分数和第一次的相同，但是在测试集上正确召回的更多了，感觉30应该是过拟合了，取20更合理。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.3 RBF核SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV(cv=5, error_score='raise',\n",
      "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False),\n",
      "       fit_params=None, iid=True, n_jobs=1,\n",
      "       param_grid={'C': [35, 36, 37, 38, 39, 40, 40.1, 40.2, 40.3, 40.4, 40.5, 40.6, 40.7, 40.8, 40.9, 41, 41.1, 41.2, 41.3, 41.4, 41.5, 41.6, 41.7, 41.8, 41.9, 42], 'gamma': [0.0001, 0.0005, 0.0006, 0.0007, 0.0008, 0.0009, 0.001, 0.0011, 0.0012, 0.0013, 0.0014, 0.0015, 0.0016, 0.0017, 0.0018, 0.0019, 0.002]},\n",
      "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
      "       scoring=None, verbose=0)\n",
      "Classification report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.77      0.85      0.81       102\n",
      "          1       0.63      0.50      0.56        52\n",
      "\n",
      "avg / total       0.72      0.73      0.72       154\n",
      "\n",
      "Confusion Matrix: \n",
      " [[87 15]\n",
      " [26 26]]\n",
      "0.7817589576547231\n",
      "{'C': 40.5, 'gamma': 0.0015}\n"
     ]
    }
   ],
   "source": [
    "# Cs_rbf = [0.001, 0.01, 0.1, 1, 10, 100, 1000]   0.7785016286644951   86  26\n",
    "# gammas = [0.0001, 0.001, 0.01, 0.1, 1]  -- 100, 0.001\n",
    "\n",
    "# Cs_rbf = [0.001, 0.01, 0.1, 1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100,\n",
    "#           110, 120, 130, 140, 150, 160, 170, 180,\n",
    "#           190, 200, 1000]                        0.7817589576547231   87  26\n",
    "# gammas = [0.0001, 0.0005, 0.0006, 0.0007, 0.0008, 0.0009, 0.001,\n",
    "#           0.0011, 0.0012, 0.0013, 0.0014, 0.0015, 0.0016, 0.0017,\n",
    "#           0.0018, 0.0019, 0.002, 0.01, 0.1, 1] -- 50, 0.0013\n",
    "\n",
    "# Cs_rbf = [41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,\n",
    "#           56, 57, 58, 59, 60]   0.7817589576547231  87 26\n",
    "#gammas = [0.0001, 0.0005, 0.0006, 0.0007, 0.0008, 0.0009, 0.001,\n",
    "#           0.0011, 0.0012, 0.0013, 0.0014, 0.0015, 0.0016, 0.0017,\n",
    "#           0.0018, 0.0019, 0.002]       -- 41  0.0015\n",
    "\n",
    "Cs_rbf = [35, 36, 37, 38, 39, 40, 40.1, 40.2, 40.3, 40.4, 40.5,\n",
    "          40.6, 40.7, 40.8, 40.9, 41, 41.1, 41.2, 41.3, 41.4, 41.5,\n",
    "          41.6, 41.7, 41.8, 41.9, 42]\n",
    "gammas = [ 0.001, 0.0011, 0.0012, 0.0013, 0.0014, 0.0015, 0.0016, 0.0017,\n",
    "          0.0018, 0.0019, 0.002]\n",
    "parameters_rbf = {'C' : Cs_rbf, 'gamma' : gammas}\n",
    "SVC3 = SVC(kernel = 'rbf')\n",
    "grid_rbf = GridSearchCV(SVC3, parameters_rbf, cv = 5)\n",
    "grid_rbf.fit(X_train, y_train)\n",
    "\n",
    "y_predict_rbf = grid_rbf.predict(X_test)\n",
    "\n",
    "print(grid_rbf)\n",
    "print(\"Classification report: \\n\", classification_report(y_test, y_predict_rbf))\n",
    "print(\"Confusion Matrix: \\n\", confusion_matrix(y_test, y_predict_rbf))\n",
    "\n",
    "print(grid_rbf.best_score_)\n",
    "print(grid_rbf.best_params_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "调整C和gamma的范围对结果没什么明显影响，尝试添加class_weight。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV(cv=5, error_score='raise',\n",
      "       estimator=SVC(C=1.0, cache_size=200, class_weight='balanced', coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False),\n",
      "       fit_params=None, iid=True, n_jobs=1,\n",
      "       param_grid={'C': [35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46], 'gamma': [0.0016, 0.0017, 0.0018, 0.0019, 0.002]},\n",
      "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
      "       scoring=None, verbose=0)\n",
      "Classification report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.82      0.80      0.81       102\n",
      "          1       0.63      0.65      0.64        52\n",
      "\n",
      "avg / total       0.76      0.75      0.75       154\n",
      "\n",
      "Confusion Matrix: \n",
      " [[82 20]\n",
      " [18 34]]\n",
      "0.7654723127035831\n",
      "{'C': 45, 'gamma': 0.0017}\n"
     ]
    }
   ],
   "source": [
    "Cs_rbf = [35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]\n",
    "gammas = [0.0016, 0.0017, 0.0018, 0.0019, 0.002]\n",
    "parameters_rbf = {'C' : Cs_rbf, 'gamma' : gammas}\n",
    "SVC3 = SVC(kernel = 'rbf', class_weight = 'balanced')\n",
    "grid_rbf = GridSearchCV(SVC3, parameters_rbf, cv = 5)\n",
    "grid_rbf.fit(X_train, y_train)\n",
    "\n",
    "y_predict_rbf = grid_rbf.predict(X_test)\n",
    "\n",
    "print(grid_rbf)\n",
    "print(\"Classification report: \\n\", classification_report(y_test, y_predict_rbf))\n",
    "print(\"Confusion Matrix: \\n\", confusion_matrix(y_test, y_predict_rbf))\n",
    "\n",
    "print(grid_rbf.best_score_)\n",
    "print(grid_rbf.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "加入balanced class weight之后，表现跟线性svm一样，也是分数降低但测试集正样本召回率提高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x90400176a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_scores =  np.array(grid_rbf.cv_results_[ 'mean_test_score' ]).reshape(len(Cs_rbf),len(gammas))\n",
    "\n",
    "x_axis = np.log10(Cs_rbf)\n",
    "for i, gamma in enumerate(gammas):\n",
    "    plt.plot(x_axis, test_scores[:,i], label= gamma)\n",
    "\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'accuary' )\n",
    "plt.legend(loc = 'lower right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
